Content
# ChatExcel MCP Server - Enterprise Enhanced v2.1.1
> **Latest Update (2025-06-19)**: pandas import issue completely fixed, project structure optimized, enterprise features fully ready
> 🚀 **Enterprise-grade Excel Intelligent Processing and Data Analysis MCP Server** - High-performance data processing solution based on FastMCP
**chatExcel-mcp** is an enterprise-grade Excel intelligent processing server based on MCP (Model Context Protocol), providing powerful Excel file analysis, data processing, formula calculation, and visualization capabilities.
[](https://python.org)
[](https://github.com/jlowin/fastmcp)
[](https://golang.org)
[](LICENSE)
[](pyproject.toml)
[](https://pypi.org/project/formulas/)
[](#-security-considerations)
[](#-performance-optimization)
[](#-operation-and-maintenance-tools)
## 🚀 Core Features
### 📊 31 Professional MCP Tools
- **Data Reading and Metadata Analysis** (2 tools): Intelligent encoding detection, structure analysis
- **Data Processing and Execution** (9 tools): Secure code execution, parameter recommendation, template generation
- **Data Visualization** (3 tools): Interactive chart generation (Chart.js)
- **Data Validation and Quality Control** (12 tools): Multi-level quality inspection, intelligent cleaning
- **Excel Formula Processing** (5 tools): Formula parsing, compilation, execution, verification
### 🏗️ Dual-Engine Architecture
- **Python Engine**: Traditional processing based on pandas/openpyxl, supporting complex data analysis
- **Go Engine**: High-performance concurrent processing, suitable for large-scale data processing (optional)
### 🧮 Excel Formula Engine (New)
- **Formula Parsing**: AST syntax analysis and security verification, supporting complex nested formulas
- **Formula Compilation**: Code generation and dependency analysis, optimizing execution performance
- **Formula Execution**: Secure execution environment and result verification, supporting context calculation
- **Dependency Analysis**: Dependency graph generation and cycle detection, avoiding calculation deadlocks
- **Formula Verification**: Syntax checking and risk assessment, ensuring formula security
### 🔍 Data Quality Control (Enhanced)
- **Multi-Level Quality Inspection**: Comprehensive verification of data integrity, consistency, and accuracy
- **Intelligent Data Cleaning**: Automated data cleaning and format standardization
- **Batch Processing**: Parallel processing of multiple Excel files, improving processing efficiency
- **Advanced Extraction**: Multi-condition data extraction and content analysis
- **Intelligent Merging**: Multi-table data merging and configuration processing
- **Character Format Conversion**: Automated character format conversion and rule configuration
### 🛡️ Enterprise-Grade Security
- **Code Security**: AST analysis and function whitelist, preventing malicious code execution
- **Execution Sandbox**: Isolated execution environment, protecting system security
- **Access Control**: Fine-grained access control and operation audit
- **Dependency Scanning**: Intelligent dependency analysis and security vulnerability detection
### ⚡ Performance Optimization
- **Intelligent Caching**: Multi-level caching strategy, reducing repeated calculations
- **Concurrent Processing**: Asynchronous task execution, improving processing speed
- **Memory Management**: Large file block processing, optimizing memory usage
- **Error Recovery**: Automatic retry mechanism and health monitoring
## 📋 Project Overview
ChatExcel MCP Server is a powerful model context protocol (MCP) server designed for Excel file processing, data analysis, and visualization. The project integrates the best data processing libraries from the Python ecosystem and provides high-performance Excel operation capabilities through the Go excelize library.
### 🎯 Core Features
- **31 Professional MCP Tools** - Covering the entire lifecycle of Excel data processing, analysis, verification, formula calculation, and data quality control
- **Dual-Engine Architecture** - Python pandas + Go excelize hybrid processing engine
- **Excel Formula Engine** - Complete Excel formula parsing, compilation, and execution system based on the formulas library
- **Data Quality Control** - 7 professional data quality tools, supporting advanced data cleaning and verification
- **Intelligent Parameter Recommendation** - Automatic detection of Excel file structure and recommendation of optimal reading parameters
- **Enterprise-Grade Security** - Multi-layer security mechanism, code sandbox execution environment, formula security verification
- **Performance Optimization** - Caching mechanism, concurrent processing, memory optimization
- **Health Monitoring** - Complete service monitoring, logging, and error tracking
- **Visualization Support** - Interactive chart generation (Chart.js, Plotly, Matplotlib)
## 🛠️ MCP Tool List
This project provides **31 professional MCP tools**, covering the entire lifecycle of Excel data processing, analysis, verification, formula calculation, and data quality control.
### 📊 Data Reading and Metadata Tools (4)
| Tool Name | Function Description | Main Features |
|---------|----------|----------|
| `read_metadata` | CSV file metadata reading and intelligent analysis | Encoding detection, delimiter recognition, data statistics |
| `read_excel_metadata` | Excel file metadata reading and integrity verification | Multi-worksheet analysis, intelligent encoding detection |
| `excel_read_enhanced` | Enhanced Excel reading tool | Go excelize integration, intelligent parameter recommendation |
| `excel_info_enhanced` | Enhanced Excel file information acquisition | Detailed file analysis, worksheet statistics |
### 🔧 Data Processing and Execution Tools (6)
| Tool Name | Function Description | Main Features |
|---------|----------|----------|
| `run_excel_code` | Excel code execution engine | Secure sandbox, complex format parameter support, ✅ pandas import completely fixed |
| `run_code` | CSV code execution engine | Secure environment, pandas integration, ✅ enhanced execution environment |
| `excel_write_enhanced` | Enhanced Excel writing tool | Format optimization, style support |
| `excel_chart_enhanced` | Enhanced Excel chart generation | Multiple chart types, custom styles |
| `excel_performance_comparison` | Excel performance comparison analysis | Go vs Python performance testing |
| `batch_data_verification_tool` | Batch data verification tool | Concurrent processing, batch reporting |
### 📈 Data Visualization Tools (3)
| Tool Name | Function Description | Main Features |
|---------|----------|----------|
| `bar_chart_to_html` | Interactive bar chart generation | Chart.js, responsive design |
| `pie_chart_to_html` | Interactive pie chart generation | Animation effects, data labels |
| `line_chart_to_html` | Interactive line chart generation | Multi-dimensional data, trend analysis |
### 🔍 Data Validation and Quality Tools (3)
| Tool Name | Function Description | Main Features |
|---------|----------|----------|
| `verify_data_integrity` | Data integrity verification and comparison | Multiple verification modes, detailed reports |
| `validate_data_quality` | Data quality verification and improvement suggestions | Quality scoring, optimization suggestions |
| `comprehensive_data_verification_tool` | Comprehensive data verification and approval tool | Comprehensive verification, quality assessment, comparison and approval |
### 🤖 Intelligent Auxiliary Tools (3)
| Tool Name | Function Description | Main Features |
|---------|----------|----------|
| `suggest_excel_read_parameters_tool` | Excel reading parameter intelligent recommendation | Structure analysis, parameter optimization |
| `detect_excel_file_structure_tool` | Excel file structure detection | Multi-level header, data area recognition |
| `create_excel_read_template_tool` | Excel reading code template generation | Intelligent template, parameter configuration |
### 🧮 Excel Formula Processing Tools (5)
| Tool Name | Function Description | Main Features |
|---------|----------|----------|
| `parse_formula` | Excel formula parser | AST parsing, syntax analysis, security verification |
| `compile_workbook` | Excel workbook compiler | Formula compilation, code generation, dependency analysis |
| `execute_formula` | Excel formula execution engine | Secure execution, context support, result verification |
| `analyze_dependencies` | Excel formula dependency analysis | Dependency graph generation, cycle detection, impact analysis |
| `validate_formula` | Excel formula validator | Security checking, syntax verification, risk assessment |
### 🔍 Data Quality Control Tools (7) - **New**
| Tool Name | Function Description | Main Features |
|---------|----------|----------|
| `enhanced_data_quality_check` | Enhanced data quality inspection | Multi-level quality inspection, comprehensive reports |
| `extract_cell_content_advanced` | Advanced cell content extraction | Multi-type extraction, formatted content |
| `convert_character_formats` | Automated character format conversion | Batch conversion, rule configuration |
| `extract_multi_condition_data` | Multi-condition data extraction | Complex conditions, flexible screening |
| `merge_multiple_tables` | Multi-table data merging | Intelligent merging, configuration processing |
| `clean_excel_data` | Excel data cleaning | Comprehensive cleaning, quality improvement |
| `batch_process_excel_files` | Batch Excel file processing | Parallel processing, unified configuration |
---
## 📋 Workflow User Manual - 31 MCP Tool Full-Process Guide
This chapter provides a complete workflow usage guide for the 31 MCP tools, categorized and associated according to actual user scenarios and data processing workflows.
### 🎯 Data Processing Full-Flow Overview
```mermaid
flowchart TD
A[📁 Data Source] --> B[🔍 Data Exploration Stage]
B --> C[📊 Data Reading Stage]
C --> D[🔧 Data Processing Stage]
D --> E[✅ Data Verification Stage]
E --> F[📈 Data Visualization Stage]
F --> G[🧮 Formula Calculation Stage]
G --> H[🔍 Quality Control Stage]
H --> I[📤 Data Output Stage]
B --> B1[File Structure Detection]
B --> B2[Metadata Analysis]
B --> B3[Parameter Recommendation]
C --> C1[Intelligent Reading]
C --> C2[Encoding Detection]
C --> C3[Template Generation]
D --> D1[Code Execution]
D --> D2[Data Conversion]
D --> D3[Batch Processing]
E --> E1[Integrity Verification]
E --> E2[Quality Inspection]
E --> E3[Data Comparison]
F --> F1[Chart Generation]
F --> F2[Interactive Visualization]
F --> F3[Report Output]
G --> G1[Formula Parsing]
G --> G2[Formula Execution]
G --> G3[Dependency Analysis]
H --> H1[Data Cleaning]
H --> H2[Format Conversion]
H --> H3[Multi-Table Merging]
I --> I1[Excel Writing]
I --> I2[Chart Embedding]
I --> I3[Performance Comparison]
```
### 🚀 Stage 1: Data Exploration and Preparation (7 tools)
#### 📋 Use Case
When you receive a new Excel file, you first need to understand the file structure, data characteristics, and optimal reading methods.
#### 🛠️ Core Tool Combination
| Step | Tool Name | Purpose | Output |
|------|----------|----------|----------|
| 1️⃣ | `excel_info_enhanced` | Get file basic information | Worksheet list, file size, format information |
| 2️⃣ | `read_excel_metadata` | Deep metadata analysis | Data type, encoding format, statistical information |
| 3️⃣ | `detect_excel_file_structure_tool` | Intelligent structure detection | Header position, data area, merged cells |
| 4️⃣ | `suggest_excel_read_parameters_tool` | Parameter intelligent recommendation | Optimal reading parameter configuration |
| 5️⃣ | `create_excel_read_template_tool` | Generate reading template | Executable reading code template |
#### 💡 Workflow Example
```
```
### 📊 Stage 2: Data Reading and Loading (4 tools)
#### 📋 Use Case
Based on the analysis results of the exploration stage, perform efficient and accurate data reading operations.
#### 🛠️ Core Tool Combination
| Tool Name | Applicable Scenario | Core Advantage | Performance Characteristics |
|----------|----------|----------|----------|
| `excel_read_enhanced` | Standard Excel file reading | Go engine acceleration, intelligent parameters | High performance, large file support |
| `read_metadata` | CSV file metadata reading | Automatic encoding detection, delimiter recognition | Lightweight, fast response |
| `read_excel_metadata` | Excel metadata dedicated | Multi-worksheet analysis, integrity verification | Comprehensive analysis, accurate and reliable |
| `excel_performance_comparison` | Performance benchmark testing | Python vs Go performance comparison | Performance optimization, engine selection |
#### 💡 Workflow Example
```
```
### 🔧 Stage 3: Data Processing and Conversion (6 tools)
#### 📋 Use Case
Perform cleaning, conversion, calculation, and processing operations on the read data.
#### 🛠️ Core Tool Combination
| Processing Type | Tool Name | Function Description | Security Level |
|----------|----------|----------|----------|
| **Code Execution** | `run_excel_code` | Excel data code execution engine | 🔒 Sandbox isolation |
| **Code Execution** | `run_code` | CSV data code execution engine | 🔒 Secure environment |
| **Data Writing** | `excel_write_enhanced` | Enhanced Excel writing tool | ✅ Format optimization |
| **Chart Generation** | `excel_chart_enhanced` | Excel embedded chart generation | 📊 Multi-style support |
| **Batch Verification** | `batch_data_verification_tool` | Batch data verification processing | ⚡ Concurrent processing |
| **Performance Comparison** | `excel_performance_comparison` | Processing performance benchmark testing | 📈 Optimization suggestions |
#### 💡 Workflow Example
```
```
### ✅ Stage 4: Data Verification and Quality Control (10 tools)
#### 📋 Use Case
Ensure data quality, integrity, and accuracy, which is a critical link in the data processing workflow.
#### 🛠️ Core Tool Combination
##### 🔍 Basic Verification Tools (3)
| Tool Name | Verification Focus | Output Report |
|----------|----------|----------|
| `verify_data_integrity` | Data integrity and consistency verification | Detailed verification report, problem location |
| `validate_data_quality` | Data quality assessment and improvement suggestions | Quality scoring, optimization suggestions |
| `comprehensive_data_verification_tool` | Comprehensive verification and assessment | Complete verification report, quality certification |
##### 🧹 Advanced Quality Control Tools (7)
| Tool Name | Professional Field | Core Function |
|----------|----------|----------|
| `enhanced_data_quality_check` | Multi-level quality inspection | Deep quality analysis, comprehensive assessment |
| `extract_cell_content_advanced` | Content extraction analysis | Multi-type extraction, formatted processing |
| `convert_character_formats` | Character format standardization | Batch conversion, rule configuration |
| `extract_multi_condition_data` | Complex condition screening | Multi-dimensional screening, flexible configuration |
| `merge_multiple_tables` | Multi-table data integration | Intelligent merging, relationship processing |
| `clean_excel_data` | Data cleaning and optimization | Comprehensive cleaning, quality improvement |
| `batch_process_excel_files` | Batch file processing | Parallel processing, unified standards |
#### 💡 Workflow Example
```
```
### 📈 Stage 5: Data Visualization and Reporting (3 tools)
#### 📋 Use Case
Convert processed data into intuitive charts and interactive visualization reports.
#### 🛠️ Core Tool Combination
| Chart Type | Tool Name | Applicable Scenario | Technical Characteristics |
|----------|----------|----------|----------|
| **Bar Chart** | `bar_chart_to_html` | Classification data comparison, trend analysis | Chart.js, responsive design |
| **Pie Chart** | `pie_chart_to_html` | Proportion analysis, composition display | Animation effects, data labels |
| **Line Chart** | `line_chart_to_html` | Time series, trend changes | Multi-dimensional data, interactive zooming |
#### 💡 Workflow Example
```
```
### 🧮 Stage 6: Excel Formula Processing and Calculation (5 tools)
#### 📋 Use Case
Handle complex Excel formulas, perform advanced calculations, and analyze dependency relationships.
#### 🛠️ Core Tool Combination
| Processing Stage | Tool Name | Core Function | Security Features |
|----------|----------|----------|----------|
| **Parsing** | `parse_formula` | Formula syntax analysis, AST construction | 🔒 Security verification, syntax checking |
| **Compilation** | `compile_workbook` | Workbook compilation, code generation | ⚡ Performance optimization, dependency analysis |
| **Execution** | `execute_formula` | Formula secure execution, result calculation | 🛡️ Sandbox environment, context isolation |
| **Analysis** | `analyze_dependencies` | Dependency relationship analysis, impact assessment | 🔍 Cycle detection, relationship graph |
| **Verification** | `validate_formula` | Formula security verification, risk assessment | ✅ Security checking, compliance verification |
#### 💡 Workflow Example
```
```
### 🎯 Complete Workflow Integration Example
#### 📋 End-to-End Data Processing Workflow
```
```
### 📚 Best Practice Recommendations
#### 🎯 Tool Selection Strategy
1. **Small Files (<10MB)**: Use Python engine tools for quick response
2. **Large Files (>50MB)**: Prioritize Go engine tools for better performance
3. **Complex Formulas**: Must use formula processing tool chain to ensure security
4. **Batch Processing**: Use batch tools to improve efficiency
5. **High Quality Requirements**: Use complete quality control processes
#### ⚡ Performance Optimization Recommendations
1. **Cache Strategy**: Enable cache mechanism for repetitive operations
2. **Concurrent Processing**: Use concurrent tools for batch tasks
3. **Memory Management**: Use chunked processing for large files
4. **Engine Selection**: Choose the best engine based on performance test results
#### 🔒 Secure Usage Principles
1. **Code Execution**: Always execute in a sandbox environment
2. **Formula Processing**: Must perform security verification
3. **File Access**: Verify file paths and permissions
4. **Resource Limitations**: Set reasonable timeout and memory limits
---
## 🧮 Excel Formula Processing Function Details
### Function Overview
Built with `formulas==1.2.10` library, providing a complete Excel formula processing system from parsing to execution.
### Core Tool Details
#### 1. `parse_formula` - Formula Parser
```python
# Parse Excel formula and get AST structure
result = parse_formula("=SUM(A1:A10)*2", validate_security=True)
# Returns: syntax tree, function list, referenced cells, security status
```
#### 2. `compile_workbook` - Workbook Compiler
```python
# Compile Excel file into Python code or JSON structure
result = compile_workbook("/path/to/file.xlsx", output_format="python")
# Supports formats: 'python', 'json'
```
#### 3. `execute_formula` - Formula Execution Engine
```python
# Execute Excel formula in specified context
context = '{"A1": 10, "A2": 20}'
result = execute_formula("=A1+A2", context)
# Returns: calculation result, execution status, performance metrics
```
#### 4. `analyze_dependencies` - Dependency Analyzer
```python
# Analyze formula dependencies in Excel file
result = analyze_dependencies("/path/to/file.xlsx")
# Returns: dependency graph, cycle detection, impact analysis
```
#### 5. `validate_formula` - Formula Validator
```python
# Validate formula security and effectiveness
result = validate_formula("=SUM(A1:A10)")
# Returns: security assessment, syntax check, risk level
```
### Security Features
- **AST Security Analysis**: Detect potential malicious code patterns
- **Function Whitelist**: Only allow secure Excel functions
- **Reference Verification**: Verify cell reference legitimacy
- **Execution Sandbox**: Isolated formula execution environment
### Performance Optimization
- **Cache Mechanism**: Intelligent caching of parsing results
- **Concurrent Support**: Parallel processing of multiple formulas
- **Memory Management**: Chunked processing of large files
- **Error Recovery**: Graceful exception handling
---
## 📋 Version Update Log
### v2.1.1 (2025-06-19) - pandas Import Fix
**🔧 Key Fixes**
- ✅ **pandas Import Issue Completely Fixed**: Thoroughly resolve pandas import failure in MCP server
- Enhanced execution environment for `fallback_enhanced_run_excel_code` function
- Added multiple pandas and numpy reference methods (`pd`, `pandas`, `np`, `numpy`)
- Improved built-in functions and common module imports
- Enhanced error handling and return format
- ✅ **Project Structure Optimization**: Completed project file organization and structure optimization
- Moved documentation files to `record/` directory for unified management
- Cleaned redundant files and optimized directory structure
- Improved configuration files and dependency management
**🆕 New Modules**
- `enhanced_globals_config.py` - Enhanced global configuration module
- `pandas_fix_patch.py` - pandas import fix patch
- `mcp_pandas_integration.py` - MCP server integration fix module
- Complete testing and verification suite
### v2.1.0 (2025-06-18) - Enterprise-Level Enhancement
**🎉 Major Updates**
- ✅ **tabulate Library Complete Integration**: Thoroughly resolve tabulate ImportError issue, support pandas.to_markdown() functionality
- ✅ **Excel Formula Engine Enhancement**: Complete formula processing system based on formulas==1.2.10
- ✅ **31 MCP Tools**: Added 7 data quality control tools, covering complete data processing lifecycle
- ✅ **Security Mechanism Optimization**: Enhanced code execution sandbox, improved security verification mechanism
- ✅ **Performance Improvement**: Go excelize integration, cache mechanism, concurrent processing optimization
- ✅ **Health Monitoring**: Complete service monitoring, logging, and error tracking system
- ✅ **Environment Compatibility**: Improved virtual environment support and dependency checking
### v2.0.0 (2025-06-18) - Major Update
**🎉 Major Updates**
- ✅ **MCP Tool Expansion**: From 24 to 31 professional tools
- ✅ **Dual-Engine Architecture**: Python + Go high-performance processing
- ✅ **Data Validation Enhancement**: Multi-level validation and quality control
- ✅ **Visualization Upgrade**: Chart.js interactive charts
- ✅ **Security Hardening**: Code execution sandbox and permission control
- ✅ **tabulate Library Restriction Removal**: Completely removed security restrictions on tabulate library, support table formatting functionality
- ✅ **Security Configuration Optimization**: Updated security.json configuration, added tabulate to secure module list
- ✅ **Code Execution Enhancement**: Optimized secure_code_executor.py, improved code execution security
- ✅ **Testing Coverage Improvement**: Added tabulate library independent testing and MCP integration testing
- ✅ **Documentation Update**: Improved README and requirements.txt version information
## 🚀 Quick Start
### 📋 Environment Requirements
| Component | Version Requirements | Description |
|------|--------------------|------|
| **Python** | 3.11+ | Recommended Python 3.11 or higher |
| **Operating System** | macOS, Linux, Windows | Full-platform support |
| **Memory** | 4GB+ | Recommended 8GB for better performance |
| **Disk Space** | 1GB+ | Includes dependency and cache space |
| **Go** | 1.21+ (optional) | For high-performance Excel processing |
### 📦 Dependency Management
#### Core Dependencies
- **MCP Protocol**: `mcp>=1.9.4`, `fastmcp>=2.8.0`
- **Data Processing**: `pandas>=1.5.3`, `numpy>=1.26.4`, `pandasai>=2.3.0`
- **Excel Processing**: `openpyxl>=3.1.5`, `xlsxwriter>=3.2.5`
- **Machine Learning**: `torch>=2.1.0`, `transformers>=4.39.2`, `scikit-learn>=1.2.2`
- **Visualization**: `matplotlib>=3.10.1`, `seaborn>=0.13.2`, `plotly>=6.0.1`
- **Web Service**: `fastapi>=0.115.12`, `uvicorn>=0.30.6`, `gradio>=5.23.3`
#### 📦 Dependency Compatibility Description
This project has resolved the following dependency conflicts:
- ✅ **pandas Import Fix**: Completely resolved pandas import failure in MCP environment (v2.1.1)
- ✅ **Execution Environment Enhancement**: Supports multiple pandas/numpy reference methods (`pd`, `pandas`, `np`, `numpy`)
- ✅ **Torch Version**: Downgraded to `torch==2.1.0` to be compatible with `torchvision==0.16.0`
- ✅ **PandasAI Compatibility**: Upgraded to `pandasai==2.3.0` and maintained `pandas==1.5.3`
- ✅ **Pydantic Version**: Upgraded to `pydantic==2.11.7` to support MCP and other modern dependencies
- ✅ **SSL Certificate Issue**: Provided `--trusted-host` parameter solution
- ✅ **Project Structure Optimization**: Organized documentation into `record/` directory, cleaned redundant files
#### Troubleshooting
If encountering dependency conflicts, follow these steps:
1. **Check Dependency Status**
```bash
pip check
python scripts/health_check.py
```
2. **Reinstall Dependencies**
```bash
pip uninstall -y torch torchvision pandasai pandas pydantic
pip install torch==2.1.0 torchvision==0.16.0 --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host files.pythonhosted.org
pip install pandasai==2.3.0 --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host files.pythonhosted.org
pip install "pydantic>=2.7.2" --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host files.pythonhosted.org
```
3. **Verify Fix**
```bash
pip check
python scripts/health_check.py
```
### ⚡ One-Click Deployment (Recommended)
```bash
# 1. Clone project
git clone https://github.com/chatexcel/chatExcel-mcp.git
cd chatExcel-mcp
# 2. One-click deployment (automatically install dependencies, configure environment, and start service)
./start.sh
# 3. Verify deployment status
python scripts/health_check.py
```
### 🔧 Manual Deployment
```bash
# 1. Create virtual environment
python3 -m venv venv
source venv/bin/activate # macOS/Linux
# or venv\Scripts\activate # Windows
# 2. Install dependencies
pip install -r requirements.txt --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host files.pythonhosted.org
# 3. Start GO service (optional, for high-performance Excel processing)
cd excel-service
go run main.go &
cd ..
# 4. Start MCP server
python server.py
```
### 📊 Service Status Verification
```bash
# Check service health status
curl http://localhost:8080/api/v1/health # GO service
python scripts/health_check.py # Complete system check
```
### 🔧 Manual Installation
#### Step 1: Environment Preparation
```bash
# Clone project
git clone https://github.com/chatexcel/chatExcel-mcp.git
cd chatExcel-mcp
# Create virtual environment
python3 -m venv venv
# Activate virtual environment
source venv/bin/activate # macOS/Linux
# venv\Scripts\activate # Windows
```
#### Step 2: Install Dependencies
```bash
# Upgrade pip
pip install --upgrade pip
# If encountering SSL certificate issues, use the following command
pip install -r requirements.txt --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host files.pythonhosted.org
# Or install normally
pip install -r requirements.txt
# Verify installation
python3 check_dependencies.py
# Run health check script
python scripts/health_check.py
# Check dependency conflicts
pip check
```
#### Step 3: Configure Service
```bash
# Generate MCP configuration file
python3 generate_mcp_config.py
# Check environment configuration
python3 check_env.py
```
#### Step 4: Start Service
```bash
# Start standard server
python3 server.py
# Or start enhanced server (recommended)
python3 enhanced_server.py
# Run in background
nohup python3 server.py > chatexcel.log 2>&1 &
```
### 🐳 Docker Deployment
#### Use Pre-Built Image
```bash
# Pull image
docker pull chatexcel/mcp-server:latest
# Run container
docker run -d \
--name chatexcel-mcp \
-p 8080:8080 \
-v $(pwd)/data:/app/data \
-v $(pwd)/config:/app/config \
chatexcel/mcp-server:latest
```
#### Build Locally
```bash
# Build image
docker build -t chatexcel-mcp .
# Run container
docker run -d \
--name chatexcel-mcp \
-p 8080:8080 \
-v $(pwd)/data:/app/data \
chatexcel-mcp
```
### 🔍 Installation Verification
```bash
# Run health check
python3 scripts/health_check.py
# Run functional test
python3 test/quick_test.py
# Verify MCP tools
python3 comprehensive_mcp_test.py
# Check service status
curl http://localhost:8080/health
```
### 🔧 Development Guide
#### Project Structure
```
chatExcel-mcp/
├── chatexcel_mcp/ # Main source code
│ ├── __init__.py
│ ├── server.py # MCP server main file
│ ├── tools/ # Tool modules
│ │ ├── excel_tools.py # Excel operation tools
│ │ ├── chart_tools.py # Chart generation tools
│ │ └── ai_tools.py # AI analysis tools
│ └── utils/ # Utility functions
├── tests/ # Test files (created)
├── docs/ # Documentation
├── examples/ # Example files
├── scripts/ # Script files
│ └── health_check.py # Health check script
├── requirements.txt # Dependency list (updated)
├── pyproject.toml # Project configuration (updated)
└── README.md # Project description
```
#### Environment Health Check
The project includes a complete health check mechanism:
```bash
# Run complete health check
python scripts/health_check.py
```
Health check includes:
- ✅ Python version verification
- ✅ Virtual environment detection
- ✅ Dependency package version verification
- ✅ Project file structure integrity
- ✅ Server module import test
#### Version Compatibility
Current environment verified compatible:
- **Python**: 3.8+
- **Torch**: 2.1.0 (compatible with torchvision 0.16.0)
- **PandasAI**: 2.3.0 (compatible with pandas 1.5.3)
- **Pydantic**: 2.11.7 (supports MCP 1.9.4)
- **All dependencies**: No conflict status
## 🔧 MCP Configuration and Integration
### MCP Client Configuration
#### Claude Desktop Configuration
Add the following to `~/Library/Application Support/Claude/claude_desktop_config.json`:
```json
{
"mcpServers": {
"chatexcel": {
"command": "python3",
"args": ["/path/to/chatExcel-mcp2.0/server.py"],
"env": {
"PYTHONPATH": "/path/to/chatExcel-mcp2.0"
}
}
}
}
```
#### Automatic Configuration Generation
```bash
# Generate MCP configuration file
python3 generate_mcp_config.py
# View generated configuration
cat mcp_config_absolute.json
```
### Environment Variable Configuration
Create `.env` file:
```bash
# Service configuration
MCP_SERVER_HOST=localhost
MCP_SERVER_PORT=8080
MCP_LOG_LEVEL=INFO
# Excel processing configuration
EXCEL_MAX_FILE_SIZE=100MB
EXCEL_CACHE_ENABLED=true
EXCEL_GO_SERVICE_URL=http://localhost:8081
# Security configuration
CODE_EXECUTION_TIMEOUT=30
MAX_MEMORY_USAGE=1GB
SECURE_MODE=true
```
## 📖 Usage Examples
### 🔍 Basic Excel Read
```python
# Use MCP tool to read Excel file
result = await mcp_client.call_tool(
"read_excel_metadata",
{"file_path": "/path/to/your/file.xlsx"}
)
print(f"Sheet count: {result['sheets_count']}")
print(f"Data rows: {result['total_rows']}")
print(f"Encoding format: {result['encoding']}")
```
### 🤖 Intelligent Parameter Recommendation
```python
# Get optimal read parameters
params = await mcp_client.call_tool(
"suggest_excel_read_parameters_tool",
{"file_path": "/path/to/complex.xlsx"}
)
# Tool List
```python
# Execute data analysis code
analysis = await mcp_client.call_tool(
"run_excel_code",
{
"file_path": "/path/to/data.xlsx",
"code": """
# Data cleaning and analysis
df_clean = df.dropna()
summary = df_clean.describe()
correlation = df_clean.corr()
# Data quality check
missing_data = df.isnull().sum()
duplicate_rows = df.duplicated().sum()
print("=== Data Summary ===")
print(summary)
print(f"\nMissing Data: {missing_data.sum()}")
print(f"Duplicate Rows: {duplicate_rows}")
"""
}
)
```
### 📈 Chart Generation
```python
# Generate interactive bar chart
chart = await mcp_client.call_tool(
"bar_chart_to_html",
{
"labels": ["Q1", "Q2", "Q3", "Q4"],
"datasets": [
{
"label": "Sales (Million Yuan)",
"data": [120, 150, 180, 200],
"backgroundColor": "rgba(54, 162, 235, 0.6)"
}
],
"title": "2024 Quarterly Sales Report",
"options": {
"responsive": True,
"plugins": {
"legend": {"display": True}
}
}
}
)
print(f"Chart generated: {chart['filepath']}")
```
### 🧮 Excel Formula Processing
```python
# Parse Excel formula
formula_result = await mcp_client.call_tool(
"parse_formula",
{
"formula": "=SUM(A1:A10)*0.1+AVERAGE(B1:B10)",
"validate_security": True
}
)
print(f"Formula parsing successful: {formula_result['is_valid']}")
print(f"Referenced cells: {formula_result['references']}")
# Execute formula
execute_result = await mcp_client.call_tool(
"execute_formula",
{
"formula": "=A1+B1",
"context": '{"A1": 10, "B1": 20}'
}
)
print(f"Calculation result: {execute_result['result']}")
```
### 🔍 Data Quality Control
```python
# Enhanced data quality check
quality_check = await mcp_client.call_tool(
"enhanced_data_quality_check",
{
"file_path": "/path/to/data.xlsx",
"check_types": ["completeness", "consistency", "accuracy"],
"generate_report": True
}
)
print(f"Data quality score: {quality_check['quality_score']}")
print(f"Issues found: {len(quality_check['issues'])}")
# Batch data verification
batch_verification = await mcp_client.call_tool(
"batch_data_verification_tool",
{
"file_paths": [
"/path/to/file1.xlsx",
"/path/to/file2.xlsx"
],
"verification_rules": {
"check_duplicates": True,
"validate_formats": True,
"check_completeness": True
}
}
)
print(f"Batch verification completed, files processed: {batch_verification['processed_count']}")
```
## 🏗️ Project Architecture
### System Architecture Diagram
```mermaid
graph TB
A[MCP Client] --> B[FastMCP Server]
B --> C[Tool Router]
C --> D[Excel Engine]
C --> E[Data Engine]
C --> F[Chart Engine]
D --> G[Python pandas]
D --> H[Go excelize]
D --> I[openpyxl]
E --> J[Data Validator]
E --> K[Code Executor]
E --> L[Cache Manager]
F --> M[Chart.js]
F --> N[Plotly]
F --> O[Matplotlib]
P[Security Layer] --> C
Q[Monitoring] --> B
R[Logging] --> B
```
### Core Modules
#### 📁 Main File Structure
```
chatExcel-mcp/
├── server.py # Main server file (19 MCP tools)
├── enhanced_server.py # Enhanced server
├── config.py # Configuration management
├── excel_enhanced_tools.py # Excel enhanced tools
├── excel_smart_tools.py # Excel smart tools
├── data_verification.py # Data verification engine
├── comprehensive_data_verification.py # Comprehensive data verification
├── excel-service/ # Go excelize service
│ ├── main.go
│ ├── go.mod
│ └── go.sum
├── templates/ # Chart templates
│ ├── barchart_template.html
│ ├── linechart_template.html
│ └── piechart_template.html
├── scripts/ # Operation and maintenance scripts
│ ├── deploy.py
│ ├── health_check.py
│ └── maintenance.sh
├── config/ # Configuration files
│ ├── runtime.yaml
│ ├── security.json
│ └── system.json
└── tests/ # Test suite
├── unit/
├── integration/
└── performance/
```
#### 🔧 Engine Class Design
- **ExcelEnhancedProcessor**: High-performance Excel processing engine
- **DataVerificationEngine**: Data verification and quality check engine
- **ComprehensiveDataVerifier**: Comprehensive data verifier
- **SecureCodeExecutor**: Secure code executor
### Data Flow Architecture
#### Excel Processing Flow
```mermaid
sequenceDiagram
participant C as Client
participant S as Server
participant E as Excel Engine
participant G as Go Service
participant P as Python Engine
C->>S: Call excel_read_enhanced
S->>E: Route to Excel engine
E->>G: Try Go excelize
alt Go service available
G-->>E: Return high-performance result
else Go service unavailable
E->>P: Fallback to pandas
P-->>E: Return standard result
end
E-->>S: Return processing result
S-->>C: Return final result
```
#### Data Verification Flow
```mermaid
sequenceDiagram
participant C as Client
participant S as Server
participant V as Validator
participant R as Reporter
C->>S: Call verify_data_integrity
S->>V: Start verification engine
V->>V: Structure verification
V->>V: Data type check
V->>V: Completeness verification
V->>V: Statistical analysis
V->>R: Generate verification report
R-->>S: Return detailed report
S-->>C: Return verification result
```
#### Code Execution Flow
```mermaid
sequenceDiagram
participant C as Client
participant S as Server
participant SE as Security Engine
participant EX as Executor
participant M as Monitor
C->>S: Call run_excel_code
S->>SE: Security check
SE->>SE: Blacklist filtering
SE->>SE: Syntax verification
SE-->>S: Security passed
S->>EX: Sandbox execution
EX->>M: Monitor execution
M->>M: Resource monitoring
M->>M: Timeout check
EX-->>S: Return execution result
S-->>C: Return final result
```
### Performance Optimization Architecture
#### Cache Mechanism
```mermaid
graph LR
A[Request] --> B{Cache check}
B -->|Hit| C[Return cache]
B -->|Miss| D[Process request]
D --> E[Update cache]
E --> F[Return result]
G[Cache strategy]
G --> H[LRU eviction]
G --> I[TTL expiration]
G --> J[Memory limit]
```
#### Concurrent Processing
```python
# Concurrent processing example
class ConcurrentProcessor:
def __init__(self, max_workers=4):
self.executor = ThreadPoolExecutor(max_workers=max_workers)
self.semaphore = asyncio.Semaphore(max_workers)
async def process_batch(self, tasks):
async with self.semaphore:
futures = [self.executor.submit(task) for task in tasks]
results = await asyncio.gather(*futures)
return results
```
### Security Architecture Design
#### Multi-Layer Security Protection
```mermaid
graph TB
A[User Request] --> B[Input validation layer]
B --> C[Permission check layer]
C --> D[Code security layer]
D --> E[Execution sandbox layer]
E --> F[Output filtering layer]
F --> G[Audit log layer]
H[Security policy]
H --> I[Blacklist filtering]
H --> J[Whitelist verification]
H --> K[Resource limit]
H --> L[Timeout control]
```
#### Error Handling Mechanism
```mermaid
graph LR
A[Exception occurs] --> B{Exception type}
B -->|Security exception| C[Security log]
B -->|Business exception| D[Business log]
B -->|System exception| E[System log]
C --> F[Alert notification]
D --> G[User feedback]
E --> H[Operation and maintenance notification]
F --> I[Security response]
G --> J[Error recovery]
H --> K[System repair]
```
### Scalability Design
#### Plugin Architecture
```python
# Plugin interface definition
class MCPToolPlugin:
def __init__(self, name: str, version: str):
self.name = name
self.version = version
def register_tools(self, mcp_server):
"""Register MCP tools"""
raise NotImplementedError
def initialize(self, config: dict):
"""Initialize plugin"""
pass
def cleanup(self):
"""Cleanup resources"""
pass
# Plugin manager
class PluginManager:
def __init__(self):
self.plugins = {}
def load_plugin(self, plugin_class, config=None):
plugin = plugin_class()
plugin.initialize(config or {})
self.plugins[plugin.name] = plugin
return plugin
```
#### Configuration Management
```python
# Dynamic configuration example
class ConfigManager:
def __init__(self, config_path="config/"):
self.config_path = Path(config_path)
self.configs = {}
self.watchers = {}
def load_config(self, name: str) -> dict:
config_file = self.config_path / f"{name}.yaml"
with open(config_file, 'r') as f:
config = yaml.safe_load(f)
self.configs[name] = config
return config
def watch_config(self, name: str, callback):
"""Monitor configuration file changes"""
self.watchers[name] = callback
```
### Monitoring and Operation and Maintenance Architecture
#### Health Check
```python
# Health check example
class HealthChecker:
def __init__(self):
self.checks = {
"database": self.check_database,
"cache": self.check_cache,
"disk_space": self.check_disk_space,
"memory": self.check_memory
}
async def run_health_check(self) -> dict:
results = {}
for name, check_func in self.checks.items():
try:
results[name] = await check_func()
except Exception as e:
results[name] = {"status": "error", "error": str(e)}
overall_status = "healthy" if all(
r.get("status") == "healthy" for r in results.values()
) else "unhealthy"
return {
"status": overall_status,
"checks": results,
"timestamp": datetime.utcnow().isoformat()
}
```
#### Log and Monitoring
```mermaid
graph TB
A[Application Logs] --> B[Log Collector]
C[Performance Metrics] --> D[Metric Collector]
E[Error Tracking] --> F[Error Collector]
B --> G[Log Storage]
D --> H[Metric Storage]
F --> I[Error Storage]
G --> J[Log Analysis]
H --> K[Monitoring and Alerting]
I --> L[Error Analysis]
J --> M[Operations Dashboard]
K --> M
L --> M
```
## 🧪 Testing and Verification
### Running Test Suites
```bash
# Run all tests
python3 -m pytest tests/ -v
# Run specific tests
python3 comprehensive_mcp_test.py
python3 final_verification.py
# Performance testing
python3 test/performance/benchmark.py
```
### Health Checks
```bash
# Service health check
curl http://localhost:8080/health
# Detailed diagnostics
python3 diagnose_mcp_setup.py
# Excel feature verification
python3 demo_excel_features.py
```
### Core Dependency Verification
```bash
# NumPy and Pandas feature verification
python3 -c "import numpy as np; import pandas as pd; print('✅ Core dependencies are normal')"
# Excel smart feature testing
python3 test_excel_smart_features.py
# Go service connection test
python3 excel_go_client.py --test
```
## 🔒 Security Considerations
### Code Execution Security
- **Blacklist filtering**: Prohibits dangerous operations (os, sys, subprocess, etc.)
- **Sandbox environment**: Isolated code execution environment
- **Resource limitations**: Memory, CPU, and execution time limitations
- **Input validation**: Strict parameter validation and type checking
### File Access Security
- **Path validation**: Prevents directory traversal attacks
- **File size limitations**: Prevents large file attacks
- **Format verification**: Ensures file format correctness
- **Permission checks**: File read and write permission verification
### Network Security
- **HTTPS support**: Encrypted transmission
- **Authentication mechanism**: API key verification
- **Rate limiting**: Prevents DDoS attacks
- **Audit logs**: Complete operation records
## 🛠️ Maintenance Tools
### Automation Scripts
```bash
# Deployment script
./scripts/deploy.py --env production
# Health check
./scripts/health_check.py --detailed
# Maintenance script
./scripts/maintenance.sh --clean-cache
# Dependency update
./scripts/update_dependencies.sh
```
### Cache Management
```bash
# Clear cache
python3 cache_manager.py --clean
# Cache statistics
python3 cache_manager.py --stats
# Cache configuration
vim cache_config.json
```
### Log Management
```bash
# View real-time logs
tail -f logs/chatExcel.log
# Log analysis
python3 scripts/log_analyzer.py --date today
# Log rotation
logrotate config/logrotate.conf
```
## ⚡ Performance Optimization
### Memory Optimization
- **Chunked reading**: Large file chunked processing
- **Memory pool**: Object reuse mechanism
- **Garbage collection**: Active memory cleanup
- **Cache strategy**: LRU cache eviction
### Concurrency Optimization
- **Asynchronous processing**: asyncio concurrency model
- **Thread pool**: CPU-intensive task parallelism
- **Connection pool**: Database connection reuse
- **Queue mechanism**: Task queue management
### I/O Optimization
- **Batch operations**: Reduce I/O times
- **Compression transmission**: Data compression transmission
- **Pre-reading**: Intelligent data preloading
- **Cache hit**: Improve cache hit rate
## 🐛 Troubleshooting
### 📋 Quick Diagnosis
```bash
# Run comprehensive diagnostic tool
python3 diagnose_mcp_setup.py
# Check system health status
python3 scripts/health_check.py --detailed
# Verify all dependencies
python3 check_dependencies.py
```
### 🔧 Common Problem Solutions
#### 1. 🚫 Service Startup Failure
**Symptoms**: Service cannot start or exits immediately
```bash
# Check port occupancy
lsof -i :8080
# If port is occupied, kill the process or change the port
kill -9 <PID>
# Check Python environment
which python3
python3 --version
# Check dependency integrity
pip check
pip list | grep -E "fastmcp|pandas|openpyxl"
# View detailed error logs
python3 server.py --debug
# Or view log files
tail -f chatExcel.log
```
**Solution**:
```bash
# Reinstall dependencies
pip install --upgrade --force-reinstall -r requirements.txt
# Clear cache
pip cache purge
python3 -c "import shutil; shutil.rmtree('.encoding_cache', ignore_errors=True)"
# Start with a different port
MCP_SERVER_PORT=8081 python3 server.py
```
#### 2. 📊 Excel Read Failure
**Symptoms**: Unable to read Excel files or abnormal reading results
```bash
# Check file permissions and format
ls -la /path/to/file.xlsx
file /path/to/file.xlsx
# Verify file integrity
python3 -c "import openpyxl; wb=openpyxl.load_workbook('/path/to/file.xlsx'); print('File is normal')"
# Test basic reading functionality
python3 test/simple_test.py /path/to/file.xlsx
```
**Solution**:
```bash
# Repair file permissions
chmod 644 /path/to/file.xlsx
# Use a different reading engine
python3 -c "
import pandas as pd
# Try different engines
for engine in ['openpyxl', 'xlrd']:
try:
df = pd.read_excel('/path/to/file.xlsx', engine=engine)
print(f'{engine} engine succeeded')
break
except Exception as e:
print(f'{engine} engine failed: {e}')
"
# Check encoding issues
python3 utils/encoding_detector.py /path/to/file.xlsx
```
#### 3. 🔗 Go Service Connection Failure
**Symptoms**: Go excelize service cannot connect or response timeout
```bash
# Check Go service status
ps aux | grep excel-service
lsof -i :8081
# Test Go service connection
curl -v http://localhost:8081/health
telnet localhost 8081
```
**Solution**:
```bash
# Recompile Go service
cd excel-service
go mod tidy
go build -o excel-service main.go
# Start Go service
./excel-service &
# If Go is unavailable, disable Go service
export EXCEL_GO_SERVICE_ENABLED=false
python3 server.py
```
#### 4. 🔒 Permission and Security Issues
**Symptoms**: Code execution is blocked or security checks fail
```bash
# Check security configuration
cat config/security.json
# Test security mode
python3 -c "
from security.secure_code_executor import SecureCodeExecutor
executor = SecureCodeExecutor()
result = executor.execute('print(\"Hello World\")')
print(result)
"
```
**Solution**:
```bash
# Adjust security configuration (operate with caution)
vim config/security.json
# Temporarily disable strict mode (only for debugging)
export SECURE_MODE=false
python3 server.py
# Check blacklist configuration
python3 -c "from config import SECURITY_CONFIG; print(SECURITY_CONFIG['blacklisted_modules'])"
```
#### 5. 💾 Memory and Performance Issues
**Symptoms**: Insufficient memory or slow response when processing large files
```bash
# Monitor memory usage
top -p $(pgrep -f server.py)
# Check cache status
python3 cache_manager.py --stats
# Clear cache
python3 cache_manager.py --clean
```
**Solution**:
```bash
# Adjust memory limits
export MAX_MEMORY_USAGE=2GB
export EXCEL_MAX_FILE_SIZE=50MB
# Enable chunked processing
export CHUNK_SIZE=10000
python3 server.py
# Optimize cache configuration
vim cache_config.json
```
### 🔍 Debugging Tools
#### Basic Debugging
```bash
# Simple function testing
python3 test/simple_debug.py
# MCP tool testing
python3 comprehensive_mcp_test.py
# Quick verification
python3 test/quick_test.py
```
#### Advanced Debugging
```bash
# Performance analysis
python3 -m cProfile -o profile.stats server.py
python3 -c "import pstats; p=pstats.Stats('profile.stats'); p.sort_stats('cumulative').print_stats(10)"
# Memory analysis
python3 -m memory_profiler server.py
# Network debugging
netstat -tulpn | grep :8080
ss -tulpn | grep :8080
```
#### Log Analysis
```bash
# Real-time log monitoring
tail -f chatExcel.log | grep -E "ERROR|WARNING"
# Log statistics and analysis
python3 scripts/log_analyzer.py --date today --level ERROR
# Clear old logs
find . -name "*.log" -mtime +7 -delete
```
### 📞 Get Help
If the above solutions do not resolve the issue, please:
1. **Collect diagnostic information**:
```bash
python3 diagnose_mcp_setup.py > diagnosis.txt
python3 --version >> diagnosis.txt
pip list >> diagnosis.txt
```
2. **Create a minimal reproducible example**:
```bash
python3 test/create_minimal_test.py
```
3. **Submit an Issue**: Visit [GitHub Issues](https://github.com/Lillard01/chatExcel-mcp2.0/issues) and attach the diagnostic information
## 📄 License
This project is licensed under the [MIT License](LICENSE).
## 🤝 Contribution Guide
We welcome community contributions! Please follow these steps:
1. **Fork** this repository
2. Create a feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a **Pull Request**
### Development Guidelines
- Follow [PEP 8](https://www.python.org/dev/peps/pep-0008/) code style
- Add appropriate test cases
- Update relevant documentation
- Ensure all tests pass
### Code Quality Checks
```bash
# Code formatting
black .
# Code inspection
flake8 .
# Type checking
mypy .
# Security inspection
bandit -r .
```
## 📞 Contact
- **Project Maintainer**: ChatExcel Team
- **Issue Feedback**: [GitHub Issues](https://github.com/Lillard01/chatExcel-mcp/issues)
- **Feature Suggestions**: [GitHub Discussions](https://github.com/Lillard01/chatExcel-mcp/discussions)
- **Technical Support**: lillardw459@gmail.com
## 🙏 Acknowledgements
Thanks to the support of the following open-source projects:
- [FastMCP](https://github.com/jlowin/fastmcp) - MCP server framework
- [pandas](https://pandas.pydata.org/) - Data analysis library
- [openpyxl](https://openpyxl.readthedocs.io/) - Excel file processing
- [excelize](https://github.com/qax-os/excelize) - Go Excel library
- [formulas](https://github.com/vinci1it2000/formulas) - Excel formula parsing and execution engine
- [Chart.js](https://www.chartjs.org/) - Chart visualization
- [Plotly](https://plotly.com/) - Interactive charts
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